首页|Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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In recent years,various companies have started to shift their data services from traditional data centers to the cloud.One of the major motivations is to save on operational costs with the aid of cloud elasticity.This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections,without disconnecting them from the server side.This paper considers this need as a bi-objective online load balancing problem.A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity.An evolutionary multi-objective training framework is proposed to optimize the weights of the policy.Not only is the new objective of idleness reduced by over 130％more than traditional industrial solutions,but the original load balancing objective itself is also slightly improved.Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

evolutionary reinforcement learningevolutionary multi-objective optimizationload balancecloud computing

Peng YANG、Laoming ZHANG、Haifeng LIU、Guiying LI

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Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen 518055,China

Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China

Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen 518055,China

Guangdong OPPO Mobile Telecommunications Corp.,Ltd,Shenzhen 518052,China

Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen 518055,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaGuangdong Provincial Key LaboratoryProgram for Guangdong Introducing Innovative and Entrepreneurial TeamsCCF-Tencent Open FundMarket Data Cloud Joint Laboratory between Shenzhen Securities Information Co.,Ltd.and Southern University of Science and Techno

6227221062331014622507106822020B1212010012017ZT07X386RAGR20220110XB11ZC20210189

2024

10.1007/s11432-023-3895-3

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(2)
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